Comparison of Aerosol and Intranasal Challenge in a Mouse Model of Allergic Airway Inflammation and Hyperresponsiveness
Bibliographic record
Abstract
BACKGROUND: The aim was to optimize antigen challenge for induction of airway hyperresponsiveness (AHR) and inflammation in BALB/c mice sensitized to ovalbumin (OVA). Comparisons were made between mice challenged with OVA either as an aerosol or intranasally. The protocol that induced maximal AHR in BALB/c mice was thereafter tested in C57BL/6 mice. METHOD: Methacholine responsiveness was measured using the flexiVent® system to assess AHR. Inflammatory responses were investigated by histology and cell counts in bronchoalveolar lavage (BAL) fluid. RESULTS: 48 h after challenge with 1 or 6% OVA aerosols, there were similar increments in AHR and BAL cells, predominantly eosinophils. When comparing the effect of 1% OVA aerosol on AHR and cell infiltration at 24 and 48 h after challenge, the responses were similar. At 24 h, intranasal OVA administration (20-200 μg) caused a dose-dependent increase in AHR. BAL cells were increased by all intranasal OVA doses and to a greater extent than after 1% OVA aerosol challenge but without any dose dependency. Histological examination confirmed that there was an increase of eosinophils in lung tissue following either challenge. In C57BL/6 mice, baseline tissue elastance was the only functional outcome that was increased after intranasal OVA challenge. Even though the AHR response was negligible in C57BL/6 mice, a similar infiltration of BAL cells was observed in both strains. CONCLUSION: Intranasal challenge was more effective than aerosol challenge at inducing both AHR and airway inflammation in BALB/c mice. Although intranasal challenge caused airway inflammation in C57BL/6 mice, this strain is not optimal for studying AHR.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".